Java程序辅导

A Virtual Deployment Testing Environment for Enterprise
Software Systems∗
Jian Yu
Faculty of Information and
Communication Technologies
Swinburne University of
Technology
Hawthorn, VIC 3122, Australia
jianyu@swin.edu.au
Jun Han
Faculty of Information and
Communication Technologies
Swinburne University of
Technology
Hawthorn, VIC 3122, Australia
jhan@swin.edu.au
Jean-Guy Schneider
Faculty of Information and
Communication Technologies
Swinburne University of
Technology
Hawthorn, VIC 3122, Australia
jschneider@swin.edu.au
Cameron Hine
Faculty of Information and
Communication Technologies
Swinburne University of
Technology
Hawthorn, VIC 3122, Australia
chine@swin.edu.au
Steve Versteeg
CA Labs
380 St. Kilda Rd
Melbourne, VIC 3004,
Australia
steve.versteeg@ca.com
ABSTRACT
Modern enterprise software systems often need to interact
with a large number of heterogeneous systems in an enter-
prise IT environment. The distributedness, large-scale-ness,
and heterogeneity of such environment makes it difficult to
test a system’s quality attributes such as performance and
scalability before it is actually deployed in the environment.
In this paper, we present a Coloured Petri nets (CPN) based
system behaviour emulation approach and a lightweight vir-
tual testing framework for provisioning the deployment test-
ing environment of an enterprise system so that its quality
attributes, especially scalability, can be evaluated without
physically connecting to the real production environment.
This testing environment is scalable and has a flexible plug-
gable architecture to support the emulation of the behaviour
of heterogeneous systems in the environment. To validate
the feasibility of this approach, a CPN emulation model for
LDAP has been developed and applied in testing the scala-
bility of a real-life identity management system. An in-lab
performance study has been conducted to demonstrate the
effectiveness of this approach.
∗This work is supported by the ARC Linkage Project
LP100100622 Large-Scale Emulation for Enterprise Soft-
ware Systems.
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QoSA’12, June 25–28, 2012, Bertinoro, Italy.
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Categories and Subject Descriptors
D.2.5 [Software Engineering]: Testing and Debugging—
Testing tools; D.2.2 [Software Engineering]: Design Tools
and Techniques—Petri nets
General Terms
Design, Measurement, Verification
Keywords
Enterprise software systems, Deployment testing, System
emulation, Petri nets
1. INTRODUCTION
Modern enterprise software systems usually operate in a
complex environment where up to tens of thousands of di-
verse systems interact and cooperate to support the daily
operation of a large enterprise such as, for example, a multi-
national financial institution. From the perspective of a sin-
gle system as a node in this networked architecture, its oper-
ating environment, that is, the collection of systems it needs
to interact with, is inherently distributed, heterogeneous, and
large-scale. In a typical scenario, the systems in the envi-
ronment are not only distributed globally in various physical
locations, but also vary in functionality, type, and commu-
nication protocols. Furthermore, an enterprise-class system
is supposed to have the capability to concurrently serve or
interact with a large number of systems.
For example, CA’s Identity Manager [2, 6] is an enterprise-
class software system used to coordinate and manage the
many thousands of user accounts and computational re-
sources present in a large organization. Identity Manager is
capable of managing heterogeneous enterprise environments
and thus can apply access control policies to a wide range
of resources, such as, Lightweight Directory Access Proto-
col (LDAP) directories [22], email servers, Windows NT and
Unix machines etc. In practice, an Identity Manager system
101
Figure 1: Conceptual model of the virtual testing
environment.
may need to manage and control up to 10,000 computer
systems in an organization.
Quality assurance is a major concern of enterprise software
systems [5]. However, the complex operating environment
of an enterprise system brings difficulty in examining and
testing its quality attributes, such as performance and scal-
ability, before it is actually deployed in its target environ-
ment. Physically provisioning the production environment
for testing purpose is generally impractical because of both
the large number of systems involved and the geographical
distribution of these systems. Furthermore, the diversity in
the types and configurations of systems in the environment
also bring another layer of complexity to testing. Over the
years, a number of approaches have been proposed to pro-
vide executable, interactive representations of deployment
environments (cf. Section 2). However, all of these ap-
proaches fall short in providing software engineers with a
flexible tool-set to examining and testing software systems’
quality attributes.
To address these shortcomings, we propose a novel Coloured
Petri nets (CPN) [14] based system behaviour emulation ap-
proach and framework for provisioning a quality testing en-
vironment of enterprise software systems. As illustrated in
Figure 1, we use a single virtual testing environment (VTE)
to provision a system-under-test (SUT). A system residing
in the testing environment that the SUT needs to interact
with (in the context of this paper, we call such system an
endpoint system, or EP) is replaced by a virtual endpoint
system comprising a dedicated CPN model that emulates
the behaviour of a real endpoint system, and an emulation
node that executes this model.
The main benefits of our emulation based virtual testing
environment include: (i) the heterogeneity of enterprise soft-
ware environments is accommodated by providing different
behaviour models that emulate the behaviour of different
endpoint systems and then execute these models in generic
emulation nodes. A behaviour model can either be spec-
ified according to the functionality of the EP and/or the
communication protocol between the SUT and the EP, or
be derived from the interaction traces between the SUT and
the EP. (ii) The approach is expected to scale to the number
of endpoint systems typically found in large-scale enterprise
system environments. On the one hand, an emulation node
has the potential to emulate the behaviour of a large set
of EPs that have the same functionality (e.g., up to 10,000
LDAP servers) instead of just one. On the other hand, the
behaviour model could be a simplification of the real EP as
long as this model is consistent with a given testing scenario.
This means that a VTE running on a physical machine may
use much less resources than tens of thousands of real EPs
running on physical or virtual machines, respectively. (iii)
The VTE provides a closed testing environment for the SUT
because the physically unavailability or inaccessibility prob-
lem of real EPs is avoided by behaviour emulation.
In this paper, we illustrate our approach to systematically
building a CPN emulation component for the VTE from
behaviour modelling, model pruning, operation modelling, to
model integration. Although CPN has been widely used in
distributed systems modelling and analysis [14], to the best
of our knowledge, it is the first effort in applying CPN in
the emulation of endpoint systems for purpose of deployment
testing. A case study in building a CPN emulation model for
LDAP servers has been conducted in order to demonstrate
the efficiency and scalability of this approach.
The rest of the paper is organized as follows: in Section 2,
we discuss related work, followed by an introduction of the
architecture and implementation of the virtual testing envi-
ronment in Section 3. Section 4 introduces the CPN based
approach to endpoint system emulation using LDAP server
emulation as an illustrating example. In sections 5 and 6,
we present and interpret the results of our scalability exper-
iments, respectively. Finally, Section 7 concludes the paper
with a summary of the main observations and a discussion
of future work.
2. RELATEDWORK
Testing distributed systems is a complex problem [28]. For
example, Ghosh and Mathur raise nine issues in testing dis-
tributed components by pointing out that “components that
interact with a heterogeneous environment can be more eas-
ily tested by utilizing an emulation environment capable of
representing a range of different components, and scalabil-
ity and performance testing for components can be enabled
through using scalable models.”[7]
To provision a testing environment for a distributed sys-
tem or component, physical replication is clearly the most
primitive approach, which in many cases cannot well han-
dle the heterogeneity and scalability of a testing environ-
ment. Hardware virtualization tools such as VMWare [23]
and VirtualBox [26] certainly provide better management
and control over virtual testing servers and they are capa-
ble in hosting heterogeneous endpoint systems in the testing
environment. But this approach has two major limitations:
(i) for a large-scale environment, provisioning the whole en-
vironment through hardware virtualization alone is costly.
(ii) It is not possible to provision an endpoint system that is
not available to be hosted on a virtual machine (e.g., hosted
in another organization).
Specifically designed for evaluating the performance and
scalability of server software, load generation tools such as
HP’s LoadRunner [12], SLAMD Distributed Load Genera-
tion Engine [24] and the Apache software foundation’s JMe-
ter [1] are capable of representing many thousands of concur-
rent clients, transmitting requests to the server under test.
This approach is very suitable for providing performance
102
Scalability Dealing with
Heterogeneity
Behaviour
Abstraction
Interaction
Mode
Configurability
Physical Replication None Weak None Two-Way Weak
Hardware Virtualization Weak Good Machine Two-Way Good
Load Generation Tools Good Weak User One-Way Good
Stubs and Mocks Good Weak Interface Two-Way Weak
Virtual Emulation
Environment
Good Good Endpoint
system
Two-Way Good
Table 1: Capability comparison of different approaches.
diagnosis of the system-under-test and identifying the crit-
ical bottlenecks in the system. One of the limitations of
load generation tools is that they are mainly used to gener-
ate scalable autonomous client load against a reactive server
system, but they are not able to represent the complex in-
teractions between the system-under-test and the diverse
endpoint systems, respectively.
Method stubs and mock objects [4, 8] have been used
to programmatically emulate the behaviour of remote end-
point systems. With the flourish of a test-driven develop-
ment paradigm, there are currently some language-specific
mocking frameworks available such as Mockito [18] for Java,
Rspec [21] for Ruby, and Mockery [17] for PHP. The main
limitations of the stub/mock approach include: (i) the test-
ing framework is usually language specific and thus not suit-
able to provision a generic testing environment. (ii) The be-
haviour of a stub/mock is programmed in an ad hoc fashion.
It could be challenging to configure at a high level the be-
haviour of a stub or mock, and any changes to the test plan
may lead to changing the code of the stub/mock.
In Table 1, the features of different approaches discussed
above are summarized from the perspectives of provisioning
a distributed deployment testing environment. The follow-
ing features are considered in the table:
• Scalability. Referring to the relationship between the
number of systems capable of being represented using
a single physical host.
• Dealing with Heterogeneity. Referring to the abil-
ity of provisioning diverse endpoint systems.
• Behaviour Abstraction. The underlying abstrac-
tion model on which the interactions are based.
• Interaction Mode. Whether the approach can han-
dle the two-way interactions between a client and a
server.
• Configurability. Referring to the ability of provid-
ing changeable parameters to the user so that certain
characteristics of the approach can be easily changed.
A more detailed discussion of the limitations of existing ap-
proaches to enable enterprise software system analysis can
be found in [11].
From the perspective of system behaviour emulation, the
emulation logic can be directly expressed in a given host pro-
gramming language. For example, in our previous work [9,
10], the Haskell programming language [16] has been used
to approximate the behaviour of LDAP server endpoints.
In contrast to directly using a programming language, CPN
has the following benefits in specifying the emulation logic:
(i) CPN has a graphical representation, leading to easier
specification. (ii) The true concurrency property of Petri
nets makes it suitable to directly model and emulate the
behaviour of distributed systems [14]. (iii) CPN is a for-
mal modelling language, which makes it possible to directly
verify the correctness properties, such as deadlock, bound-
edness, of the behaviour model; it is also possible to model
check the behaviour model against a formal specification of
the emulated system.
A potential weakness of using CPN in the emulation en-
vironment is that models defined using CPN may not be as
efficiently executable as endpoints directly encoded in pro-
gramming language. However, the results of a case study in
emulating LDAP endpoint systems presented in Section 5
demonstrate that the run-time performance of the CPN-
based approach is sufficient for simultaneously emulating the
behaviour of up to 10,000 LDAP endpoint systems.
3. ARCHITECTURE OF THE VIRTUAL
TESTING ENVIRONMENT
As a proof-of-concept, we have implemented a prototype
of a virtual testing environment (VTE). In this section, we
will briefly introduce the main elements of the underlying
architecture. The interested reader is referred to [11] for
further details.
The architecture of the virtual testing environment is illus-
trated in Figure 2. The VTE has four main components: (i)
the network interface component for encoding and decoding
of native communication, (ii) the engine for executing the
emulated behaviour of an endpoint system, (iii) the moni-
tor component for observing and publishing the behaviour
of the nodes emulated by the engine, and (iv) the configura-
tion component for managing configurations. In the follow-
ing, we will discuss the important aspects of each of these
components in more detail.
Network Interface
The heterogeneous nature of enterprise software environ-
ments means that messages exchanged on communication
channels linking nodes may be packaged and encoded on a
native channel in numerous different ways. LDAP, for ex-
ample, encodes its messages using the basic encoding rules
of ASN.1, with some restrictions. The network interface
module of an emulator allows the encoding and decoding
concerns of native communication to be isolated from the
models used by the engine to emulate node behaviour.
The network interface consists of four entities. The native
service allows external nodes to establish new channels with
the emulator. The native conduits are responsible for facil-
itating the interaction between external nodes and the em-
103
Figure 2: Architecture of the Virtual Testing Environment.
ulator’s engine along previously established communication
channels. The interface scheduler is responsible for schedul-
ing when the various services and conduits get an oppor-
tunity to perform some activity. The interface monitor is
responsible for observing and reporting the behaviour of the
interface.
Engine
The engine is the host of the Virtual Endpoint Systems, the
Node Scheduler, and the Monitor. Every Virtual Endpoint
System has an emulation node for executing the actual emu-
lation logic specified by the behaviour models. Services and
channels are responsible for interacting with the network in-
terface. The Node Scheduler is for selecting and triggering
the execution of emulated nodes at particular points in time,
and the Engine Monitor is for observing and publishing the
behaviour of emulation nodes.
Monitoring and Configuration
The monitoring component is responsible for processing the
data published by the different internal monitors of the emu-
lator, including the interface and engine monitors. The em-
ulator monitor registry listens to the information published
by the other monitors and stores and organizes it. Other
modules interested in emulation data can then extract the
specific data which they are interested in from this registry
for further processing and use. The visualization compo-
nent extracts data required to visualize an emulation from
the registry. This component uses the data to produce use-
ful visualization both during and after an emulation. The
logging component extracts information required for logging
an emulation. This logged information can be used for post-
emulation analysis, helping to pinpoint the root cause of
some observed failure in an external software systems under
test.
Finally, the configuration component is responsible for
managing the configuration of an emulator. This includes
specifying which native services are going to be provided and
how they map to specific engine services. The configuration
module also provides a means to select and refine specific
node models for an enterprise software tester’s purposes.
4. EMULATING ENDPOINT SYSTEMS
WITH PETRI NETS
In this section, we discuss the CPN-based modelling and
emulation approach to emulating the behaviour of endpoint
systems.
Petri nets is a formalism suitable for modelling the be-
haviour of systems with characteristics of concurrency, com-
munication and synchronization such as network protocols,
distributed systems, and workflow systems [19, 20]. Coloured
Petri nets (CPN) is a backwards compatible extension of
Petri nets and is often viewed as a graphical language for
constructing executable models of concurrent software sys-
tems [13]. Coloured Petri nets combine Petri nets and the
functional programming language CPN ML. CPN Tools [14]
is an environment for editing, executing, and the verification
of properties of Coloured Petri nets and has been widely used
in both, academia and industry.
There are several benefits in using CPN Tools as the be-
haviour modelling and emulation environment: (i) the con-
currency feature of CPNs makes it possible to model the
behaviour of a set of endpoint systems that have the same
functionality using a number individual tokens running on
just one Petri net structure. This may contribute signifi-
cantly to the scalability of the VTE. (ii) With CPN ML as
a full-fledged programming language, we are able to model
the detailed message exchange between the SUT and the
VTE. (iii) CPN Tools can efficiently execute CPN models
as we will further demonstrate in Section 5. Last but not
least, the graphical features and verification capabilities of
CPN Tools facilitate both the design and verification of the
CPN-based emulation models, respectively.
In general, our approach of emulating a specific endpoint
system can be divided into four steps: (i) use CPNs to de-
scribe the states and operations/transitions of endpoints to
obtain a high-level behaviour model, (ii) prune the high-
level behaviour model to get a subset of the functionality
104
Figure 3: Emulating the behaviour of n LDAP server endpoints.
that is pertinent to the testing, (iii) model the behaviour of
each operation, and (iv) connect the model to auxiliary I/O
models (also in CPN) responsible for interacting with the
VTE and then put it in the VTE as a behaviour model for
the engine component.
In the following, we use an industry-motivated scenario to
explain the details of these four steps. This scenario is to
emulate the behaviour of a variable number n (up to 10,000)
of LDAP servers [22]. It is derived from the requirements
of CA’s quality assurance team for the purpose of testing
Identity Manager. As we can see in Figure 3, we want to
emulate the behaviour of n LDAP servers with one node
in the engine of the virtual testing environment, and the
behaviour of this node is specified by a single CPN model.
4.1 High Level Behaviour Modelling
In general, a system model comprises two types of ele-
ments: elements that represent state and elements that rep-
resent change. For example, programming languages use
variables to represent state and assignment statements to
represent change [29]. Petri nets use places (graphically rep-
resented as circles) to represent state and transitions (graph-
ically represented as rectangles) to represent change. A high
level behaviour model of a system can be constructed by
identifying the key operations of the system, followed by
specifying the state changes caused by the execution of these
operations.
The Lightweight Directory Access Protocol (LDAP) [22] is
a communication protocol widely used in enterprise environ-
ments, in particular to manage access to the computational
resources of an enterprise. In general, an LDAP server is
a communication end-point which allows a client to search
and modify a persistent directory (tree) structure hosted on
that server.
A CPN model of LDAP that captures the key LDAP op-
erations1 that are used by Identity Manager and the state
changes related to these operations is shown in Figure 4.
The regular interaction generally begins with the client es-
tablishing a connection to an LDAP server and transmitting
a bind request with some authentication details. The LDAP
server then issues a bind response to the client which indi-
1In order to enhance the presentation of the modelling steps,
we have omitted some of the “administrative” LDAP opera-
tions from the model.
cates either the success or failure of the corresponding bind
operation.
Once bound, a client can issue a number of different re-
quests to search and/or modify the LDAP server with add
or delete operations. The majority of the requests a client
can make result in just a single response from the server,
indicating either success or failure of this request. A search
request, on the other hand, can result in zero or more search
result entries which match the search criteria received by the
server. After zero or more result entries have been transmit-
ted, the search completion is indicated by the transmission
of a search result done message. Finally, an LDAP session
is usually closed by the client issuing an unbind request.
It is worth noting that (i) because behaviour modelling is
done manually with application specific requirements, it is
highly possible that modelling the same protocol may create
different models depending on the situation, and (ii) proper-
ties of the created models, such as liveness and boundedness,
can be verified by the CPN Tools [14].
4.2 Model pruning
After a high-level behaviour model of an endpoint system
has been specified, a software engineer may prune the model
by removing places and transitions that are not pertinent to
a particular testing activity. This step helps to shrink the
model and thus makes the model more efficient to execute.
A software engineer can remove places and/or transitions
of the model to get a subnet of the original Petri net. To
ensure the behavioural consistency between the subnet and
the parent net, the reachable marking set (cf. Definition 4.2
below) of the subnet should be covered by that of its parent
net. In the following, we will illustrate the model pruning
process and define behavioural consistency between a subnet
and its parent net.
The reader may note that pruning primarily affects the
net structure of a Coloured Petri net, the structure which is
shared by most members of the Petri nets family [15]. There-
fore, in order to enhance readability of this section, we define
the pruning action based on the net structure definition of
Petri nets and not given the full definition of Coloured Petri
nets.
Definition 1 (Petri net Structure). A Petri net struc-
ture N = (S, T ;F ) is a 3-tuple where S ∩ T = ∅; S ∪ T %= ∅;
F ⊆ S × T ∪ T × S; dom(F ) ∪ cod(F ) = S ∪ T , where
105
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e
e
e
e
e
e
e
e
e
e
e
e
e
e
e
e
e
e
t3
search
t2t1
unbind
deleteadd
bind
More
Entries
UNIT
Search
Failed
UNIT
Not
Found
UNIT
Found
UNIT
Delete
Failed
UNIT
Delete
Success
UNIT
Add 
Failed
UNIT
Add 
Success
UNIT
Authentication
Failed
UNIT
Bound
UNIT
Unbound
()
UNIT
Figure 4: A high level Petri net model of LDAP.
e
e
e
e
e
e
e
ee
e
e
e
e
e
e
e
t2t1
unbind
deleteadd
bind
UNIT
Delete
Success
UNIT
Add 
Success
UNIT
Bound
UNIT
Unbound
()
UNIT
t3
Found
search
Figure 5: Pruned LDAP model.
dom(F ) = {x | ∃y : (x, y) ∈ F}, cod(F ) = {y | ∃x : (x, y) ∈
F}.
Definition 2 (Pruning). Pruning a Petri net structure
N1 = (S1, T1;F1) by removing a set of elements X ⊂ S1∪T1
from N1 produces another net N2 = (S2, T2;F2) where S2 =
S1−X, T2 = T1−X, and F2 = F1− ((S2×T1)∪ (T1×S1)).
We call N2 a subnet of N1.
Because a Petri net is a state transition system, its be-
haviour can be defined by its reachable marking set:
Definition 3 (Reachable Marking Set). For a Petri
net structure N = (S, T ;F ), its reachable marking set [M0〉
is the smallest set that satisfies the following two conditions:
• M0 ∈ [M0〉; and
• if M ′ ∈ [M0〉, t ∈ T,M ′[t〉M , then M ∈ [M0〉.
M : S -→ V is denoted as the marking function from S to a
multiset V , M0 as the initial marking of N , and “〉” as the
corresponding firing function.
Behavioural consistency is defined based on the coverabil-
ity between two Petri net structures [19]:
Definition 4 (Behavioural Consistency). For two
Petri net structures N1 and N2, N1 is behavioural consis-
tent with N2 iff:
• MN10 ≤MN20 ; and
• ∀Mi ∈ [MN10 〉, there is a Mj ∈ [MN20 〉 where Mi ≤Mj .
According to Definition 4, if N1 is behaviourally consis-
tent with N2, then any of the markings of N1 is covered
value
n-1n (key, value)
key
key
(key, value)
(key, value)
n
n-1
(key, value)
(key, value)
n+1n
x
x
Clear
Remove
Contains
Add
Found
Fusion Found VALUE
Remove 
Parameter
Fusion DelParam KEY
Removed
KEYxVALUE
Size
0
INT
Contains
Parameter
Fusion SearchParam KEY
Container
KEYxVALUE
Add
Parameter
Fusion AddParam KEYxVALUE
(a) The generic collection model
sid
(sid, ADD, s2)
(s, value)
sid
(sid, msg)
Add
input (msg, sid);
output (s2, s, value);
action
let
 val x = extractKeyValue(msg);
 val key = sid^"+"^(#1 x);
 val y = extractMsgID (msg);
 val y = genAddResponse (y);
in
  (y, key, #2 x)
end;
Response
Fusion Response RESPONSE
KeyValue
Fusion AddParam KEYxVALUE
Add 
Request
Fusion AddReq SIDxMSG
Binded
Fusion Binded
SID
(b) The Add operation model
Figure 6: The generic collection model.
by a marking of N2. It is worth noting that behavioural
consistency can be checked using CPN Tools.
Usually, pruning can be done by removing the alternative
flows of a function whilst keeping the main flow. A similar
technique is also used in defining the main sequence flow of
a use case in requirements engineering [3].
For example, in the LDAP Petri net model in Figure 4,
we have grayed the states that belong to exceptions and
alternative flows. If we prune these states, we get a subnet
as shown in Figure 5. This subnet is behaviourally consistent
with its parent net if the initial marking of the parent net
only includes a number of units in the unbound place. The
reader may note that different versions of subnets may be
derived from pruning the same net model based on different
testing requirements.
106
4.3 Operation modelling
After a subnet has been derived by (possibly) pruning the
initial high-level model, the next step is to define the de-
tailed CPN model for each operation in the subnet. Because
we only keep the main function flow in the subnet that is
pertinent to specific testing scenarios (as illustrated in Fig-
ure 5), the detailed modelling of the operations could also be
simplified. For LDAP, we have removed all exception han-
dling functionality, and also the multiple entry results in the
search operation. Therefore, for any keyword-based query,
only a single entry will be returned on match. We use a
generic key-value map data structure to emulate the modifi-
cation and search operations, respectively. Figures 6(a) and
6(b) show the Petri net models for the generic map data
structure and the add operation. In Figure 6(a), the Con-
tainer place is used to store  pair entries, and
the Contains and Remove transitions are used implement
keyword based entry search and delete, respectively. In Fig-
ure 6(b), if there is a new LDAP add request message, the
Add transition checks if the endpoint (identified by sid) has
been bound, then the new entry is added to the container
and the corresponding response message is generated.
Besides these two models, we also defined CPN models
for the LDAP operations bind, unbind, delete, and search.2
For modularity purpose, we use fusion sets [14] to interface
between Petri net models.
4.4 Model integration
After the complete Petri net-based emulation model of an
endpoint has been constructed, we need to integrate this
model into the VTE. Because the CPN models are executed
by the CPN Tools Simulator, and CPN Tools does not sup-
port direct interaction with its simulator, we incorporate
Access/CPN [27] as the middleware between the VTE en-
gine and the CPN Tool Simulator for interactions between
the VTE engine and the CPN Tools Simulator, respectively.
Access/CPN is a framework for external applications to in-
tegrate CPN models and to interact with the CPN Tools
Simulator. It provides both Standard ML and Java inter-
faces. In our case, the Java interfaces of Access/CPN have
been used to interact with the CPN Tools Simulator where
the CPN models are executed.
As is shown in Figure 7, the channel component in the en-
gine is responsible for communicating with the Access/CPN
component, and the Access/CPN component communicates
with the CPN Tools simulator. We use  to
uniquely identify an endpoint, and use  plus
 to uniquely identify a message in the CPN
emulation model. In addition to the core emulation logic
model, we also have two supporting models: a Request
Reader for parsing the incoming messages and distributing
them to the appropriate operation model, and a Response
Writer for generating the correct outgoing messages. The
details of the Request Reader CPN model for LDAP is
shown in Figure 8, where LDAP request messages are dis-
tributed to corresponding models based on operation type.
In order to verify that the CPN model introduced in this
section is behaviourally consistent with the LDAP specifi-
cation, we performed both unit and integration tests. Unit
testing was conducted by triggering the Read Request tran-
2The complete CPN package can be downloaded at: http:
//www.ict.swin.edu.au/personal/jianyu/cpn/Ldap.cpn
Figure 7: Integrating the CPN emulation models.
sition to read an actual LDAP request message that was
generated by Identity Manager, and then checking if the
model generates a correct response message. The request
message types include bind, unbind, add, delete, and search.
Integration testing was conducted by sending a sequence of
request messages to the model and checking if all the re-
sponse messages were generated correctly in the correspond-
ing sequence, and if the model was in a consistent state after
executing an sequence.
5. EVALUATION
In order to quantify the scalability of CPN-based endpoint
models, we performed an initial in-lab performance study of
the CPN-based LDAP emulation node (CPN-LDAP). All
experiments were conducted on a Windows XP system with
Intel Core2 Duo CPU@3.00GHz and 3GB of memory.
The purpose of the experiments was to collect data on
how fast CPN-LDAP running on a specific hardware con-
figuration can respond when emulating a large number of
endpoint systems. The results of the experiments can be
used to pinpoint the threshold number of endpoint systems
that a physical machine can hold when running CPN-LDAP,
and if the threshold number is less than the required num-
ber of endpoints, then more than one physical machine may
be provisioned to run multiple instances of the VTE. It is
worth noting that the data collected are based on the re-
sponse times of the CPN emulation engine, and not of the
whole VTE.
Two scenarios were devised as the sequence of message
exchange between the system under test (Identity Manager)
and each virtual endpoint:
1. Bind→Add→Search→Delete→Unbind
2. Bind→Add*3→Search→Delete→Add
→Search*2→Delete*3→Unbind
The first sequence is a simple iteration over the core LDAP
operations, while the second sequence reflects the message
interaction patterns we observed when Identity Manager is
managing an LDAP server endpoint. The system under test
uses n channels to simultaneously interact with CPN-LDAP.
That is, if CPN-LDAP is emulating n endpoint systems,
then a maximum of n requests may be received by CPN-
LDAP at a time.3 In order to measure the actual response
3This is the worst case scenario because of simultaneous
arrival of messages. We have also tested the scenario where
the n requests were sequentially issued, which resulted in a
marginally better performance.
107
if optype = DELETE then 1`(sid, msg) else empty
if optype = SEARCH then 1`(sid, msg) else empty
if optype = ADD then 1`(sid, msg) else empty
request
if optype = UNBIND then 1`(sid, msg) else empty
if optype = BIND then 1`(sid, msg) else empty
(sid,optype, msg)
Distribute
Delete Request
Fusion DeleteReq SIDxMSG
Search Request
Fusion SearchReq SIDxMSG
Add Request
Fusion AddReq SIDxMSG
Unbind Request
Fusion UnbindReq SIDxMSG
Bind Request
Fusion BindReq SIDxMSG
In
REQUEST
Read Request
Figure 8: The Request Reader CPN model.
time of CPN-LDAP without considering delays at the sys-
tem under test side, the request of the next operation is sent
right after the response of the current request is received.
The results of the experiments are given in Figure 9. Fig-
ure 9(a) shows the total execution time of a specific number
of endpoint systems (i.e. the time to execute all steps of the
given scenarios for all endpoint systems), whilst Figure 9(b)
shows the corresponding average execution times.
The results of the experiments reveal that the execution
time of CPN-LDAP with regards to the number of endpoint
systems is not linear for both scenarios. Further experi-
ments are needed though to identify whether the execution
times increase exponentially (as Figure 9(a) may suggest)
and what specifically causes this non-linear increase.
For the first scenario, CPN-LDAP used 52 seconds to ex-
ecute the scenario for 4,000 endpoints (with on average app.
13 ms for each endpoint), whilst it took 325 seconds to ex-
ecute the scenario for 10,000 endpoint systems (with on av-
erage app. 32.5 ms per endpoint). For the second, more
realistic scenario, CPN-LDAP used 140 seconds to execute
the scenario for 4,000 endpoints (with on average app. 35 ms
per endpoint), whist 2115 seconds were needed to execute
the scenario for 10,000 endpoints (with on average 211.5 ms
for each endpoint). Discussions with quality assurance engi-
neers at CA revealed that this performance was considered
to be acceptable in an industrial setting, but further exper-
iments will be needed to get a better understanding of the
run-time performance and scalability of endpoint models.
Based on the encouraging results of the CPN-LDAP per-
formance experiments, we used the VTE to validate the scal-
ability of CA’s Identity Manager (IM) in managing up to
10,000 LDAP endpoints. IM was scripted to acquire each
endpoint, explore the endpoint, add a new user at the end-
point, and modify an existing user, very similar to (but not
100% identical) the second scenario used for the CPN-LDAP
performance evaluation. The memory usage, total CPU us-
age and response time of IM was recorded for the experi-
ments (for further details of the IM scalability experiment,
refer to [25]).
The experiment confirmed that IM is able to satisfactorily
scale to manage this number of endpoints. The experiment
also confirmed that a VTE can indeed scale up to 10,000
endpoint systems and be effectively used for quality assur-
ance purposes in an industrial setting.
(a) Total time
(b) Average time per endpoint
Figure 9: Performance of the CPN-LDAP engine
with regards to the number of endpoints.
6. DISCUSSION
Using an emulation-based approach to provisioning a vir-
tual deployment testing environment for distributed enter-
prise software system may bring several benefits as follows:
firstly, the diverse physical endpoint systems are abstracted
as virtual endpoint software components in the VTE, which
could potential save both resources and effort in implement-
ing and maintaining a testing environment. Secondly, the
emulation-based approach is inherently scalable, considering
that a virtual endpoint is an abstraction (or more specif-
108
ically, a simplification) of a real endpoint system, and a
physical machine running the VTE may be able to host a
large number of virtual endpoints. Thirdly, the heterogene-
ity of endpoint systems can be accommodated by providing
a unique behaviour model for each type of system in an envi-
ronment. Finally, physically unavailable or inaccessible end-
point systems can also be emulated as a virtual endpoint in
the VTE. We are currently exploring scenarios with hetero-
geneous and/or temporarily unavailable endpoints as part
of our further work.
An emulation-based approach also has its limitations: firstly,
extra effort is needed to implement/model a virtual end-
point, either manually or automatically (e.g., based on record-
ings of network traces). Secondly, extra effort may be needed
for testing or verifying the correctness of a virtual endpoint
model itself considering it is not a mature commercial prod-
uct as most likely the real endpoint system is.
As to our current implementation of the CPN-based ap-
proach to modelling the behaviour of endpoint systems, be-
sides the nice features brought by the graphical notations,
concurrency nature, and formalness of CPN, it also has some
limitations: firstly, the current implementation relies on the
simulator component of the CPN Tools software to execute
the endpoint model, and each simulator can only run a single
CPN model at a time, which means we need to run one sim-
ulator instance for each CPN model in the VTE. Secondly,
only the Microsoft Windows version of the CPN Tools soft-
ware is updated and maintained, which means that we may
be restricted on the type of platform the VTE can run on.
Finally, the architecture of the VTE has been defined in
such a way that it is scalable itself. For example, if the run-
time performance of a given endpoint model does not allow
for sufficient scale on a single host, then with little extra
effort, the VTE can be deployed on multiple physical hosts,
enabling the emulation of an increased number of endpoint
systems.
7. CONCLUSIONS AND FUTUREWORK
In this paper, we have presented a Coloured Petri net-
based virtual testing environment for effectively provisioning
the complex testing environment of an enterprise software
system. A CPN endpoint system behaviour emulation ap-
proach has been developed and successfully applied on provi-
sioning the scalability testing environment of an industrial-
scale identity management suite. The proposed approach is
capable of emulating the behaviour of endpoint systems at
a desired level of abstraction with acceptable performance,
and a model pruning technique is proposed to reduce the
complexity of the emulation models.
We are currently working on the virtual testing environ-
ment along several directions, including evaluating the work
intensity of creating a emulation model, (semi-)automatically
generating behaviour model from interaction sequence traces
in order to alleviate the effort of manually creating an em-
ulation model, deriving formal temporal logic specifications
from the protocol specification and then model checking the
consistency between the emulation model and the specifi-
cation, and also optimizing the performance of the CPN
emulation component. We are currently also exploring the
applicability of the approach on additional enterprise-scale
protocols and systems, respectively.
Acknowledgments
We would like to thank the Australian Research Council,
CA Technologies and in particular CA Labs for their on-
going support of, and contributions to, this research. We
would also like to thank Dr. Michael Westergaard at Eind-
hoven University of Technology, for sharing the Access/CPN
source code and his help in setting up the Access/CPN de-
velopment environment in Eclipse.
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